This section provides background information related to the present disclosure which is not necessarily prior art.
HTTP-based Adaptive Streaming (HAS) has emerged as the dominant framework for video streaming mainly due to its simplicity, firewall friendliness, and ease of deployment. HAS solutions rely on popular protocols, such as HTTP and TCP, and on standard network services supported currently by the best-effort Internet. An important example of HAS is the well-known and popular MPEG Dynamic Adaptive Streaming over HTTP (DASH) standard that is supported by many major industry players. Consequently, HAS has been receiving a great deal of attention, and many efforts have been proposed toward improving its performance and addressing many of its technical challenges.
Some of the primary challenges that have been highlighted and studied by recent efforts include HAS's inefficiency in using network resources, and its inherent reliance on TCP, which leads to key issues such as delays and a highly undesirable oscillatory behavior that changes the streamed video bitrate and its quality profile rather frequently. Recent studies have proposed improving the performance of HAS technology by improving the rate adaption algorithm and its approach for estimating available bandwidth. Meanwhile, the popularity of multimedia applications and the unprecedented growth of video traffic over the best-effort Internet have arguably aggravated DASH's challenges and led to a noticeable increase in the frequency and severity of network congestion events. All of these challenges and issues have resulted in well-documented problems in degrading the end user's Quality-of-Experience (QoE). More importantly, and as highlighted recently by key marketing studies, continued degradation in consumer's QoE could lead to a negative impact on the overall HAS and Over-The-Top (OTT) video market in a very significant way.
It is well known that delivery of optimal video over variable bandwidth networks, such as the Internet, can be achieved by exploiting the multi-priority nature of video content. A primary example of a video-streaming framework that can achieve consistent high-quality video delivery is based on multi-priority queuing of the video packets. In particular, DiffServ has emerged as the leading multi-priority video queuing solution. Under DiffServ, the video stream is divided into sub streams that are marked with different dropping precedence. As packets travel in the DiffServ network, low priority packets are dropped during congestion. To reduce queue management overhead and overall network complexity, the number of DiffServ priority queues is limited to only a few.
Another related framework is based on single network node that utilizes Active Queue Management (AQM) in conjunction with Priority Dropping, or AQM-PD. The single node may use single or multiple queues. Using a single queue with a priority dropping mechanism causes significant overhead when compared with HAS and DiffServ. When multi queue is used it is limited to few to reduce complexity. Consequently, despite the broad support for DiffServ within network routers, it has not been utilized in a significant way, and certainly not at the level that HAS based solutions have been employed and used.